Small Language Models and Neuro-Symbolic AI in Zonal Architectures: Federated Low-Rank Adaptation (Fed-LoRA) for Regional Behavior Modeling

Abstract

The automotive industry is undergoing a fundamental change, shifting from domain-centric Electrical/Electronic (E/E) architectures to Zonal Architectures designed for the Software-Defined Vehicle (SDV). This shift enables high-performance edge computing but imposes strict constraints on power, thermal management, and resource contention for mixed-criticality tasks. At the same time, autonomous systems must adapt to diverse regional driving habits ranging from strict traffic-law compliance in Western Europe to the chaotic, negotiation-heavy traffic of South Asia posing challenges for existing centralized training approaches. This report introduces a comprehensive framework that combines Small Language Models (SLMs) for semantic reasoning, Neuro-Symbolic AI (NeSy) for safety validation, and Federated Low-Rank Adaptation (Fed-LoRA) for bandwidth-efficient, privacy-preserving ongoing learning. We examine the hardware capabilities of advanced zonal controllers (e.g., NXP S32G3, TI Jacinto 7) and show that, despite having less raw data-processing power than data center GPUs, their NPU-accelerated designs are adequate for quantized SLMs when optimized with techniques such as Federated Silver Bullet (Fed-SB). Additionally, we propose a hierarchical "Regional Behavior" learning model in which a Neuro-Symbolic safety shield ensures that universal traffic rules are followed, while the SLM adapts to local cultural norms via low-rank parameter updates. This hybrid architecture balances the need for local customization with the essential safety guarantees required in next-generation autonomous vehicle mobility.